Mansi Mathur

Someone lost their job at Amazon when they were expecting their first child and looking forward to being a parent. Now their whole world is upside down. Someone got laid off in October, fought hard to find an internal role, and still got impacted in the next round. Someone who spent 10 years in the company, worked across three teams, and managed people with care, also lost their job today. It happens quickly. You get an email, the next thing you know you are locked out of the system, badge stopped working, chats gone. It feels like you were never part of the place to begin with. Layoffs are the worst part of this industry, and you can do almost nothing to prevent them once a list is made. A few things I want to say to anyone who got impacted today: 1. A spreadsheet decision is not a measure of your intelligence, your kindness, or the value you created for users and teammates. Please be kind to yourself. 2. Take time to process. Cry, vent, sleep. You are a human who just had their safety rug pulled, not a robot who must instantly “bounce back.” 3. The projects you shipped, the skills you built, the people you helped, all of that is real. A layoff cuts pay, not your track record. Do not feel like you’re not strong enough to find your way again, you are. 4. Tell friends, ex colleagues, mentors what happened. Ask for referrals, mock interviews, resume reviews, even just calls where you talk about anything except work. If you were not impacted, check on the ones who were. Nobody chooses to be on the receiving end of that email. The least we can do is remind them that the email is not the end of their story.

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Had a great time attending the Product Thinking and the Future of EPD(Engineering, product and Design) as an Engineering Leader event hosted by ELC today! A big thank you to ELC and Commure for organizing this thoughtful and engaging session—and to the amazing panel, including Dhruv Parthasarathy, Nissim Lehyani, James Tyack,Patrick Gallagher, for sharing their candid insights on how the traditional boundaries between Engineering, Product, and Design are rapidly evolving in the age of AI. Here are a few takeaways that really stuck with me: 🔹 Companies are shifting away from static PRDs and increasingly using prototypes as a discovery tool—validating ideas with customers early and often. 🔹 Hiring is changing: it’s less about checking boxes and more about finding problem solvers and clear thinkers who can adapt and lead. 🔹 When it comes to LLMs, everyone has their own “AHA moment.” Until then, keep exploring. You’ve essentially got the smartest person in your pocket—start using it to solve real problems. Excited to bring these insights back to my team and continue experimenting at the intersection of product, engineering, and AI! #EngineeringLeadership #AI #ProductThinking #ELC #LLM #TechLeadership #Prototyping #EPD

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Shantanu Narayen’s 18 year tenure as CEO at Adobe is a reminder of how rare long-term stewardship has become in Silicon Valley. During his time with the company, the company’s stock increased more than 6x and its business model fundamentally changed. The most important decision of his tenure was moving Adobe from selling boxed software like Creative Suite to a subscription platform through Creative Cloud. At the time it was controversial. Today, it is considered one of the most successful SaaS transitions in the history of enterprise software, turning Photoshop, Illustrator, and Acrobat into one of the most profitable recurring software ecosystems ever built. The respect he commands across the industry says a lot about how difficult that transformation actually was. Microsoft CEO Satya Nadella even posted publicly congratulating Narayen on a “legendary run,” praising both his leadership and the way he expanded what creators and businesses could do with software. In an industry that is often intensely competitive, that kind of recognition from peers is not given lightly. There are only a few CEOs in the world who truly get to define one era of software and then help set the stage for the next. Narayen did it by turning Adobe into a SaaS powerhouse. However, the next leader will have to answer a harder question. What does a creative software company look like when AI becomes part of the creative process itself? #Adobe #Leadership #SaaS #AI #Technology

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6 months into my job at Amazon, I broke production and thought this was the day my career ended because the company lost a lot of revenue...This is the story of one of the biggest learnings of my career. I was on an ad tech team. I was trying to learn the ad pipeline, went a bit too confident with a change, and triggered a failure. For 7–8 hours, ads were not being served. The moment I realised what had happened, my heart just dropped. I pinged my senior, called it out clearly, and stayed glued to the incident channel. Here is what surprised me. No one shouted. The first questions were: - “What changed?” - “How do we restore?” - “How do we stop this from spreading?” We rolled back. Checked the data. Verified that the pipeline was healthy again. Only after things were stable did the real work begin. That was the retrospective. We walked through the timeline, and I explained exactly what I did. We realised there was a big documentation gap. Some critical behavior of the pipeline lived only in people’s heads, not in any design doc or runbook. The conversation was not “Why did you mess this up.” It was “How did the system allow this to happen and how do we make sure the next person does not fall into the same trap?” I wrote the COE (Correction of Error) doc. My senior manager liked how honest and detailed it was. He told me, “You made a serious mistake. You also did the right things after. You reported it quickly, stayed on the fix, and left the system stronger than you found it.” That has stayed with me ever since. A healthy engineering culture does not pretend failures will never happen. It assumes they will. So it rewards people who: - Surface problems early instead of hiding them - Take ownership of their changes - Care enough to improve docs and processes after an incident - Focus on learning, not on finding a scapegoat Looking back, that outage was expensive for the company, but priceless for me. It taught me that good teams judge you not only by the bugs you ship, but by how you behave when those bugs show up in production. You will break something important at some point in your career. The question is not “How do I avoid mistakes forever?” It is “When it happens, will I have the courage to own it and the maturity to learn from it?”

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My first six months at Amazon AGI at CBG in the role of Senior Machine Learning Data Associate, a milestone that offers a meaningful opportunity for reflection. Over the past six months, this experience has been a period of significant professional growth, continuous learning, and valuable collaboration. Being part of the Amazon AGI at CBG team has provided an environment where innovation, collaboration, and high standards of excellence are deeply embedded in everyday work. Contributing to machine learning data processes and supporting the development of reliable and responsible AI systems has been both intellectually stimulating and professionally rewarding. Reflecting on this journey so far, a few key lessons stand out: 1. Growth Through New Challenges Stepping into unfamiliar challenges and embracing responsibility has been instrumental in developing both confidence and capability. Meaningful growth often begins just beyond one’s comfort zone. 2. The Importance of Continuous Learning Working alongside highly skilled colleagues has been an invaluable source of learning. Each discussion, review, and collaborative effort contributes to a deeper understanding of machine learning systems, data quality, and problem-solving approaches. 3. Supportive and Thoughtful Leadership I feel fortunate to be part of a team where leadership genuinely supports professional growth. The managers within the team create an environment that encourages curiosity, ownership, and development. I am especially grateful to my manager for the guidance, encouragement, and support they have provided, recognising individual perspectives and helping create opportunities to continue growing. 4. Curiosity as a Driver of Progress Maintaining intellectual curiosity has been central to this journey. Exploring evolving areas such as responsible AI, improving workflows, and continuously refining technical understanding keeps the work both engaging and meaningful. 5. Focusing on Impact in the Present In a dynamic and forward-looking environment like Amazon, the most effective approach is to focus on delivering meaningful work today while learning from the remarkable people around us. As I look ahead, I remain committed to continuing to learn, contribute, and grow, while supporting the broader mission of building impactful and responsible machine learning systems. #Amazon #AI #MachineLearning #ProfessionalGrowth #ContinuousLearning #DataScience

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Is the "Search & Browse" era of E-commerce officially evolving into something entirely new? 🛒🤖 I just finished watching an incredible deep dive with Manav Garg (Together Fund) on Think School. As a developer in e-commerce space, one takeaway really stood out: The transition to Agentic Commerce. For years, the industry has been obsessed with perfecting filters and recommendation engines. But the next big leap isn't a better UI, it’s the "headless" interaction between AI agents. (Timestamp: 18:05) The Shift: From UI to Infrastructure In the near future, customers may not spend 20 minutes scrolling. Instead, personal AI agents will negotiate with brand agents to find the best deal based on a user's specific goals and budget. (Timestamp: 15:08) What this means for the future of our craft: Logistics is the new Front-end: If the "Browse" layer becomes automated, the real competitive edge moves to the physical layer i.e supply chain, speed, and fulfillment infrastructure. This is where vertically integrated platforms really shine. (Timestamp: 21:31) The Rise of "Orchestration": With tools like Cursor and AI-assisted coding, we're moving away from boilerplate and toward building complex "Orchestration Layers" that allow these agents to talk to our platforms seamlessly. (Timestamp: 08:30) Efficiency as a Feature: As we integrate LLMs, the focus moves to making them profitable through smarter token usage and caching, turning "hype" into sustainable margins. (Timestamp: 36:52) We often focus on the 95% of time users spend browsing. (Timestamp: 17:52) But as that time shrinks, the platforms that win will be the ones built for agents, not just humans. Are we ready for Agentic Commerce, or are we still building for a world that's quickly moving past the search bar? Video Source: https://lnkd.in/ghfmbqw3 #Ecommerce #SoftwareDevelopment #AI #AgenticCommerce #ThinkSchool #Innovation

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FINALLY!! In addition to the push from government, wall street, and customers, there is now a push from employees too!! In a bold move, over 1,000 Amazon (India) employees are raising concerns about the company's rapid AI expansion. They're calling for sustainable practices and ethical guidelines to protect jobs and the environment. (You can read more about their demands here: https://lnkd.in/gxrnRZKs) It's crucial for tech giants to balance innovation with responsibility. As we embrace AI, let's ensure it's a force for good, not a source of harm. What are your thoughts on this?

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From Product Manager to Music Producer (Sort Of) After I built apps with Python, I made Diwali music with AI. And honestly? I have a whole new respect for musicians. What I Created: 🪔 Diwali Festive Track via Udio A celebratory instrumental celebrating the festival of lights 🎭 Full Song with Lyrics via AIVA Complete track with vocals, verses, and choruses—the real test of lyric-beat synchronization 🎼 Musical Production via Suno Experimenting with melody, rhythm, and structure refinement The Reality Check: Making "music" is easy. Making good music? That's an entirely different game. What I Actually Learned: 1. BPM IS EVERYTHING (Beats Per Minute)— How fast your song moves For my Diwali track, I aimed for ~110 BPM—upbeat enough to feel celebratory, but not so fast it loses the traditional vibe. Getting this wrong? Your song feels "off" even if you can't explain why. 2. Lyrics + Beats = MARRIAGE, NOT ROOMMATES You can't just write lyrics and slap them onto a beat. Every syllable needs to land on the rhythm. Every verse needs breathing room. Every chorus needs to hit at the right energy point. My first attempt had lyrics crashing into beats like a traffic accident. Took 4-5 iterations to get the sync right. When you add actual lyrics and vocals, every misalignment becomes glaringly obvious. 3. Music Production is a Craft (AI is Just the Brush) AI tools like Udio, AIVA, and Suno are incredible—they generate audio in seconds. But here's what they don't do: - Tell you why your song sounds flat - Fix awkward lyric placement - Guide you on structure (verse-chorus-bridge) - Make creative decisions about mood and energy I still have SO much to learn about: Audio mixing | Melody composition | Harmonic structure | Genre conventions AI accelerates execution. It doesn't replace core music understanding. 4. Iteration is the Real Skill Generate → Listen → Cringe → Refine → Repeat I went through probably 15+ variations before landing on something I wasn't embarrassed to share. Each tool taught me different lessons: Udio: Instrumental mood and energy AVIA: Lyric-vocal synchronization Suno: Production polish and structure Tech Stack: 🪔 Diwali Festive Track: Udio 🎤 Full Song with Lyrics: AIVA 🎼 Musical Refinement: Suno 🤖 Prompting: Iterative refinement with genre/mood descriptors ⚡ BPM Management: Trial and error (mostly error) This time it wasn't just about making music. It was about understanding a completely different modality of AI—and realizing how much craft still matters. Yes, AI democratizes creation. But it also reveals how much skill goes into making something truly good. What looks effortless is the result of years of understanding rhythm, harmony, structure, and emotion. Huge shoutout to Revathi Raghunath, Sumit Kumar Singh and AceAI.Club for making me a music producer, with zero theory fluff, and all building. #GenerativeAI #AIMusic #CreativeAI #BuildInPublic #AIForEveryone #ContinuousLearning

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